class_docstr: Display mismatch between raw types between datasets provided. In case if mismatch found, mark the row with a warning.
  
  The report requires :py:class:`~autogluon.eda.analysis.dataset.RawTypesAnalysis` analysis present.
  
  Parameters
  ----------
  headers: bool, default = False
      if `True` then render headers
  namespace: str, default = None
      namespace to use; can be nested like `ns_a.ns_b.ns_c`
  
  Examples
  --------
  >>> import autogluon.eda.analysis as eda
  >>> import autogluon.eda.visualization as viz
  >>> import autogluon.eda.auto as auto
  >>> auto.analyze(
  >>>     train_data=..., test_data=...,
  >>>     anlz_facets=[
  >>>         eda.dataset.RawTypesAnalysis(),
  >>>     ],
  >>>     viz_facets=[
  >>>         viz.dataset.DatasetTypeMismatch()
  >>>     ]
  >>> )
  
  See Also
  --------
  :py:class:`~autogluon.eda.analysis.dataset.RawTypesAnalysis`
code: def _render(self, state: AnalysisState) -> None:
      df = pd.DataFrame(state.raw_type).sort_index()
      warnings = df.eq(df.iloc[:, 0], axis=0)
      df["warnings"] = warnings.all(axis=1).map(
          {True: "", False: "warning"}
      )
      df.fillna("--", inplace=True)
      df = df[df["warnings"] != ""]
  
      if len(df) > 0:
          self.render_header_if_needed(state, "Types warnings summary")
          with pd.option_context(
              "display.max_rows", 100 if len(df) <= 100 else 20
          ):
              self.display_obj(df)
  
imports: from typing import Any, Dict, List, Optional
  import pandas
  from pandas import DataFrame
  from ..state import AnalysisState
  from .base import AbstractVisualization
  from .jupyter import JupyterMixin
label_desc: Use image node_id 3 for calling the DatasetTypeMismatch obj's underlying member method code with example usage: obj._render(state) without return types